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Water 2013, 5(1), 13-28; doi:10.3390/w5010013

A Data Driven Approach to Bioretention Cell Performance: Prediction and Design

1
Department of Mechanical Engineering, University of Victoria, PO Box 1700, Stn CSC, Victoria BC V8W 2Y2, Canada
2
Department of Civil Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary AB T2N 1N4, Canada
3
Department of Civil Engineering, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7A 5E1, Canada
*
Author to whom correspondence should be addressed.
Received: 24 November 2012 / Revised: 13 December 2012 / Accepted: 28 December 2012 / Published: 8 January 2013
(This article belongs to the Special Issue Sustainable Urban Water Management)
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Abstract

Bioretention cells are an urban stormwater management technology used to address both water quality and quantity concerns. A lack of region-specific design guidelines has limited the widespread implementation of bioretention cells, particularly in cold climates. In this paper, experimental data are used to construct a multiple linear regression model to predict hydrological performance of bioretention cells. Nine different observed parameters are considered as candidates for regressors, of which inlet runoff volume and duration, and initial soil moisture were chosen. These three variables are used to construct six different regression models, which are tested against the observations. Statistical analysis showed that the amount of runoff captured by a bioretention cell can be successfully predicted by the inlet runoff volume and event duration. Historical data is then used to calculate runoff volume for a given duration, in different catchment types. This data is used in the regression model to predict bioretention cell performance. The results are then used to create a design tool which can assist in estimating bioretention cell size to meet different performance goals in southern Alberta. Examples on the functionality of the design tool are provided.
Keywords: bioretention; multiple linear regression; urban runoff; hydrology; low impact development; design bioretention; multiple linear regression; urban runoff; hydrology; low impact development; design
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Khan, U.T.; Valeo, C.; Chu, A.; He, J. A Data Driven Approach to Bioretention Cell Performance: Prediction and Design. Water 2013, 5, 13-28.

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